Yuxian Gu
Artificial Intelligence · 2020–2026
A researcher in the Science corpus: 9 papers; trusts 162 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 470 citations into it; #28,507 of 35,345 by trust here, #5,359 by citations
- Natural Language Processing — 227 citations into it
- Computer Vision and Pattern Recognition — 219 citations into it
- Large Language Models — 166 citations into it
- Efficiency & Compression — 55 citations into it
- Prompting & Reasoning — 37 citations into it; #246 of 359 by trust here, #262 by citations
- General Social Sciences — 12 citations into it
Whom Yuxian Gu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alec Radford
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Ilya Sutskever
- Alex Graves
- Alex Krizhevsky
- Kaiming He
- Yann LeCun
- Jacob Devlin
Who trusts Yuxian Gu overall
- Zixuan Ke
- Chujie Zheng
- Xiao Liu
- Hunter Lang
- Tianxiang Sun
- Minlie Huang
- Yisheng Song
- Haokun Liu
- Mingkai Deng
- Jiaxu Zhao
Papers from Yuxian Gu's perspective
Ordered by how strongly the researchers Yuxian Gu trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Pre-Trained Models: Past, Present and Future (2021) — 1,095 citations
- PPT: Pre-trained Prompt Tuning for Few-shot Learning (2021) — 499 citations
- CPM-2: Large-scale Cost-effective Pre-trained Language Models (2021) — 101 citations
- AgentBench: Evaluating LLMs as Agents (2023) — 1,063 citations
- CPM: A Large-scale Generative Chinese Pre-trained Language Model (2020) — 132 citations
- DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence (2026) — 347 citations
- EVA2.0: Investigating Open-domain Chinese Dialogue Systems with Large-scale Pre-training (2022) — 60 citations
- EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training (2021) — 58 citations
- Train No Evil: Selective Masking for Task-guided Pre-training (2020) — 68 citations